Math > Merge two gathers

Merging/combining two seismic gathers (pre & post stack)

Merging seismic gathers requires matching geometry, sampling, amplitude, and phase so two datasets behave like a single continuous volume in both pre- and post-stack domains.

Pre-Stack Seismic Gathers (Shot / Receiver / CMP Gathers)

Purpose

•Combine acquisition patches

•Merge surveys from different vintages

•Create continuous coverage before processing

•Restore missing or incomplete gathers

Requirements / Steps

1.Match geometry

oRebuild or reorganize headers

oEnsure consistent SP, RP, CMP numbers

oEnsure identical coordinate reference system (CRS)

2.Match sampling parameters

oSame sample interval (dt)

oSame record length

oSame number of samples

3.Match trace headers

oSequence numbers

oOffset

oSource & receiver positions

oCDP (CMP) locations

oAzimuth, fold, channel numbers

4.Match amplitude & phase

oApply amplitude scaling

oPhase rotation if needed (phase comparison QC)

5.Sort to common domain
Example: sort both datasets to CMP gathers or shot gathers before merging.

6.Concatenate gathers

oAppend in the correct order

oRebuild trace sequence numbers

oCheck for duplicates or missing traces

7.QC

oFold map

oOffset distribution

oTrace continuity

oPhase and amplitude consistency

Post-Stack Seismic Gathers (2D/3D Volumes)

Purpose

•Splicing two seismic lines

•Merging 3D volumes from different surveys

•Extending survey coverage

•Time-lapse comparisons

Key Requirements

1.Coordinate alignment

oSame CRS / projection

oSame bin size (inline & crossline spacing)

oSame grid orientation / azimuth

oSame starting inline/crossline numbers

2.Vertical consistency

oSame sample rate (dt)

oSame time/depth domain

oSame datum and static corrections applied

oSame filter phase (zero-phase / minimum-phase)

3.Amplitude balancing

oAGC / RMS balancing

oSpectral balancing

oMatch energy between volumes

oPhase matching if needed

4.Phase consistency

oUse phase comparison

oApply global or local phase rotation

oEnsure interpretable continuity across merge zones

5.Blending or Feathering

oSmooth transitions in overlapping areas

oReduce boundary artifacts

oUse weighted averaging in overlaps

6.Grid Resampling

oResample both datasets onto the same grid

oUse interpolation (nearest, bilinear, spline)

7.Final merge

oMosaic the two volumes into one consistent cube

3. Common Problems When Merging Gathers

•Different CRS (UTM/WGS/Everest)

•Different bin size or survey azimuth

•Time shift mismatch

•Phase mismatch

•Amplitude mismatch

•Different processing sequences

•Overlapping zones with seams

•Trace misalignment or geometry conflicts

4. QC After Merge

•Check continuity of reflectors

•Examine amplitude and phase consistency

•Inline/crossline continuity

•Histogram match

•Spectrum match

•Fold & coverage maps (pre-stack)

Input gather 1 - connect/reference to the Output gather of 1st input gather

Input gather 2 - connect/reference to the Output gather of 2nd input gather for merging

Position of window { Constant, Variable } - select the options from the drop down menu.

Stitch position - provide the position where it should be merged.

Horizon item - in case the position of window is variable, then the user should provide the horizon. Connect/reference to the output horizon(s).

Layer (0-based) - by default, 0.

Window size - specify the position of the window size. By default, 100.

Interpolation params - this section deals with the horizon interpolation.

Map step X - provide the step size in x direction

Map step Y - provide the step size in y direction

Smooth window X - provide the smoothing window size in x direction

Smooth window Y - provide the smoothing window size in y direction

Interpolation type { ABOS, Kriging } - choose the interpolation type from the drop down menu. By default, ABOS.

Interpolation type - ABOS - Artificial Bounded Object Structure or ABOS is a specialized interpolation method where it uses an artificial bounded structure like network of polygons to model and interpolate the elevations information to create the topography map.

Interpolation type - Kriging - is a statistical interpolation method based on the assumption that the spatial data points are correlated. It creates an optimal surface that minimizes prediction errors by considering both the distance between data points and their spatial correlation.

Kriging covariance type { Exponential, Spherical, Gaussian } - select the kriging covariance type from the drop down menu.

Exponential - This can be used where the subsurface properties change abruptly over small distances.

Spherical - The Spherical model is useful when the data exhibits a more gradual spatial correlation up to a certain range and then behaves independently beyond that range. When subsurface structures have coherent properties within a certain distance but lose coherence at greater distances.

Gaussian - The Gaussian model is often applied in cases where there is a long-range, gradual spatial dependence between data points. It’s suitable for data where the spatial dependence does not drop off abruptly, such as when seismic properties gradually change over larger distances or in homogenous subsurface areas.

Kriging range - If two data points are closer than the kriging range, they are considered spatially correlated and will influence each other's estimated values. If the distance is greater than the range, their influence on each other will be ignored. If the distance between two data points is more than 500 then it assumes that the data beyond that limit there is no correlation

Kriging number of points - Total number of data points used to estimate/interpolate the unknown point/sample. The more the kriging points the better result however the computation time also increase with this.

Skip - By default, FALSE(Unchecked). This option helps to bypass the module from the workflow.

Output gather - generates the merged output gather.

There is no information available for this module so the user can ignore it

In this example workflow, we are working on two post stack data sets. Both the datasets must have same number of channels/fffid/sps/, record length, sample interval etc.

In this exercise, we are applying a random noise attenuation procedure on one of the dataset and the 2nd dataset is having little noise in the shallow region. We would like to merge the random noise attenuated output with the 2nd input data at a certain time.

Also, in this workflow, we are covering both the options of constant and variable Position of the window. In case of the constant, it is a standard procedure of providing the time at which the merge takes place. In case of the variable, the user needs to provide the horizon information. Based on the horizon, it will perform the merging.

Constant

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In the above example workflow, RNA output gather is considered as Input 1 & Read seismic traces module output gather is considered as Input 2.

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Make the necessary references/connections to Merge two gathers modules.

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Setup the parameters as per the requirement and execute the module.

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Variable

In case of the variable position of the window, the user MUST provide the horizon information. If the user has an existing horizon then it can be called by using load item. Otherwise, the user has to pick the horizon using "Horizon picking" module and provide the output horizon to the Horizon item parameter.

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Make the necessary reference/connections as mentioned in the previous section (Constant). Next go to parameters, and change the position of the window from Constant to Variable. Later, connect/reference the Horizon item to Output horizons of Horizon picking module.

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